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Found 7 papers, 5 papers with code
Discriminating image representations with principal distortions
This metric may then be used to optimally differentiate a set of models, by finding a pair of "principal distortions" that maximize the variance of the models under this metric.
A Spectral Theory of Neural Prediction and Alignment
The representations of neural networks are often compared to those of biological systems by performing regression between the neural network responses and those measured from biological systems.
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems.
Speech Denoising with Auditory Models
Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform.
Untangling in Invariant Speech Recognition
Higher level concepts such as parts-of-speech and context dependence also emerge in the later layers of the network.
Metamers of neural networks reveal divergence from human perceptual systems
Although model metamers from early network layers were recognizable to humans, those from deeper layers were not.
Probing emergent geometry in speech models via replica theory
The success of deep neural networks in visual tasks have motivated recent theoretical and empirical work to understand how these networks operate.
